def evaluate():
gt_e, adv_e, nl_e = get_batch(batch_size=BATCH_SIZE,
noise_levels=EVAL_NOISE_LEVELS,
methods=EVAL_METHODS,
data_dict=EVAL_DICT,
eval_data=True)
gt_t, adv_t, nl_t = get_batch(batch_size=BATCH_SIZE,
noise_levels=TRAIN_NOISE_LEVELS,
methods=TRAIN_METHODS,
data_dict=TRAIN_DICT,
eval_data=False)
# noise_lvl_e = int(nl_e) / 100
# noise_lvl_t = int(nl_t) / 100
denoiser.test(groundTruth=gt_e, noisy=adv_e,
writer='test') #, noise_lvl=noise_lvl_e)
denoiser.test(groundTruth=gt_t, noisy=adv_t,
writer='train') #, noise_lvl=noise_lvl_t)
def train(steps):
for k in range(steps):
gt, adv = get_batch(eval_data=False)
regularizer.train(groundTruth=gt,
adversarial=adv,
learning_rate=LEARNING_RATE)
if k % 50 == 0:
evaluate()
regularizer.save()
def train(steps):
for k in range(steps):
gt, adv = get_batch(batch_size=BATCH_SIZE,
noise_levels=TRAIN_NOISE_LEVELS,
methods=TRAIN_METHODS, data_dict=TRAIN_DICT)
regularizer.train(groundTruth=gt, adversarial=adv,
learning_rate=LEARNING_RATE)
if k % 50 == 0:
evaluate()
regularizer.save()
def train(steps):
for k in range(steps):
gt, adv, nl = get_batch(batch_size=BATCH_SIZE,
noise_levels=TRAIN_NOISE_LEVELS,
methods=TRAIN_METHODS, data_dict=TRAIN_DICT,
eval_data=False)
rot_id = random.randint(0, 24)
assert BATCH_SIZE == 1
gt = gt.squeeze()
adv = adv.squeeze()
gt = grid_rot90(gt, rot_id)
adv = grid_rot90(gt, rot_id)
# noise_lvl = int(nl) / 100
regularizer.train(groundTruth=gt, adversarial=adv,
learning_rate=LEARNING_RATE)#,
# noise_lvl=noise_lvl)
if k % 50 == 0:
evaluate()
regularizer.save()
def evaluate():
gt, adv = get_batch(eval_data=True,
noise_levels=['01', '016'],
methods=['EM', 'SGD'])
regularizer.test(groundTruth=gt, adversarial=adv)
def evaluate():
gt, adv, nl = get_batch(batch_size=BATCH_SIZE, noise_levels=EVAL_NOISE_LEVELS,
methods=EVAL_METHODS, data_dict=EVAL_DICT,
eval_data=True)
# noise_lvl = int(nl) / 100
regularizer.test(groundTruth=gt, adversarial=adv)#, noise_lvl=noise_lvl)
def evaluate():
gt, adv = get_batch(batch_size=BATCH_SIZE, noise_levels=EVAL_NOISE_LEVELS,
methods=EVAL_METHODS, data_dict=EVAL_DICT)
regularizer.test(groundTruth=gt, adversarial=adv)